Examples with Large Data
21 August 2025
# reticulate::use_condaenv("wompwomp_env", required = TRUE) # !!! erase
# devtools::load_all() # !!! erase
library(wompwomp) #!!! uncomment
library(dplyr)
library(ggplot2)
library(ggalluvial)
library(ggforce)
library(igraph)
library(tibble)
library(tidyr)
library(reticulate)
set.seed(42)/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
Set up our python environment
wompwomp::setup_python_env()/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
use_temp_dir <- FALSE
use_temp_dir <- (use_temp_dir || !interactive()) # ensure temp_dire when non-interactive always
output_dir <- ifelse(!use_temp_dir, here::here("vignettes", "output"), file.path(tempdir(), "vignette_output"))
dir.create(output_dir, recursive = TRUE, showWarnings = FALSE)/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
GOT
url <- "https://raw.githubusercontent.com/MattLunkes/GoT_Affiliations/master/got_char.csv"
got_file <- file.path(output_dir, "got_char.csv")
graphing_columns <- c("Origin", "Starting.Affiliation", "End.of.S1", "End.of.S2", "End.of.S3", "End.of.S4", "End.of.S5", "End.of.S6", "End.of.S7")
if (!file.exists(got_file)) {
download.file(url, got_file, method = "curl")
}
df <- read.csv(got_file)/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "none", default_sorting = "alphabetical", coloring_algorithm = "left", color_bands = FALSE, verbose = TRUE, output_plot_path = file.path(output_dir, "GoT_unsorted_alphabetical.pdf"), min_text = 0.01, save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 488## m = 9## a = 183## k_1 = 13## k_2 = 12## k_3 = 17## k_4 = 16## k_5 = 14## k_6 = 15## k_7 = 16## k_8 = 11## k_9 = 8## K_sum = 122## K_prod = 12546293760## Sorting data with sorting_algorithm=none## Determining crossing edges objective (to disable, use verbose==FALSE)## crossing edges objective = 123924## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/GoT_unsorted_alphabetical.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "none", default_sorting = "reverse_alphabetical", coloring_algorithm = "left", color_bands = FALSE, verbose = TRUE, output_plot_path = file.path(output_dir, "GoT_unsorted_reverse_alphabetical.pdf"), min_text = 0.01, save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 488## m = 9## a = 183## k_1 = 13## k_2 = 12## k_3 = 17## k_4 = 16## k_5 = 14## k_6 = 15## k_7 = 16## k_8 = 11## k_9 = 8## K_sum = 122## K_prod = 12546293760## Sorting data with sorting_algorithm=none## Determining crossing edges objective (to disable, use verbose==FALSE)## crossing edges objective = 123924## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/GoT_unsorted_reverse_alphabetical.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "none", default_sorting = "increasing", coloring_algorithm = "left", color_bands = FALSE, verbose = TRUE, output_plot_path = file.path(output_dir, "GoT_unsorted_increasing.pdf"), min_text = 0.01, save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 488## m = 9## a = 183## k_1 = 13## k_2 = 12## k_3 = 17## k_4 = 16## k_5 = 14## k_6 = 15## k_7 = 16## k_8 = 11## k_9 = 8## K_sum = 122## K_prod = 12546293760## Sorting data with sorting_algorithm=none## Determining crossing edges objective (to disable, use verbose==FALSE)## crossing edges objective = 193466## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/GoT_unsorted_increasing.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "none", default_sorting = "decreasing", coloring_algorithm = "left", color_bands = FALSE, verbose = TRUE, output_plot_path = file.path(output_dir, "GoT_unsorted_decreasing.pdf"), min_text = 0.01, save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 488## m = 9## a = 183## k_1 = 13## k_2 = 12## k_3 = 17## k_4 = 16## k_5 = 14## k_6 = 15## k_7 = 16## k_8 = 11## k_9 = 8## K_sum = 122## K_prod = 12546293760## Sorting data with sorting_algorithm=none## Determining crossing edges objective (to disable, use verbose==FALSE)## crossing edges objective = 192189## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/GoT_unsorted_decreasing.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
set.seed(42)
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "random", coloring_algorithm = "left", color_bands = FALSE, verbose = TRUE, output_plot_path = file.path(output_dir, "GoT_random.pdf"), min_text = 0.01, save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 488## m = 9## a = 183## k_1 = 13## k_2 = 12## k_3 = 17## k_4 = 16## k_5 = 14## k_6 = 15## k_7 = 16## k_8 = 11## k_9 = 8## K_sum = 122## K_prod = 12546293760## Sorting data with sorting_algorithm=random## Determining crossing edges objective (to disable, use verbose==FALSE)## crossing edges objective = 343087## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/GoT_random.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
# df['Lannister_origin'] <- df["Origin"] == 'House Lannister'
df <- df %>%
mutate(Lannister_origin = case_when(
Origin == "House Lannister" ~ 1,
Origin == "Westeros" ~ 2,
TRUE ~ 0
))
set.seed(42)
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "random", optimize_column_order = FALSE, color_boxes = FALSE, color_bands = TRUE, min_text = 0.01, verbose = FALSE, color_band_list = c("0" = "grey", "1" = "#D55E00", "2" = "#56B4E9"), color_band_column = "Lannister_origin", output_plot_path = file.path(output_dir, "GoT_random_lannister_origin.pdf"), save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
# df['Lannister_ending'] <- df["End.of.S7"] == 'Queen Cersei Lannister'
df <- df %>%
mutate(Lannister_ending = case_when(
End.of.S7 == "Queen Cersei Lannister" ~ 1,
End.of.S7 == "Other, Westeros" ~ 2,
TRUE ~ 0
))
set.seed(42)
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "random", optimize_column_order = FALSE, color_boxes = FALSE, color_bands = TRUE, min_text = 0.01, verbose = FALSE, color_band_list = c("0" = "grey", "1" = "#D55E00", "2" = "#56B4E9"), color_band_column = "Lannister_ending", output_plot_path = file.path(output_dir, "GoT_random_lannister_ending.pdf"), save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "neighbornet", optimize_column_order = FALSE, color_bands = FALSE, coloring_algorithm = "left", min_text = 0.01, verbose = TRUE, output_plot_path = file.path(output_dir, "GoT_NN.pdf"), save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 488## m = 9## a = 183## k_1 = 13## k_2 = 12## k_3 = 17## k_4 = 16## k_5 = 14## k_6 = 15## k_7 = 16## k_8 = 11## k_9 = 8## K_sum = 122## K_prod = 12546293760## Sorting data with sorting_algorithm=neighbornet## Running neighbornet## Running 'neighbornet' for stratum order## Cycle: End.of.S1~~Balon Greyjoy, King of the Iron Islands, Origin~~House Greyjoy, End.of.S2~~Balon Greyjoy, King of the Iron Islands, End.of.S3~~Balon Greyjoy, King of the Iron Islands, End.of.S4~~Balon Greyjoy, King of the Iron Islands, End.of.S5~~Balon Greyjoy, King of the Iron Islands, End.of.S6~~Euron Greyjoy, King of the Iron Islands, Starting.Affiliation~~Deceased, End.of.S2~~Qarth, End.of.S1~~Qarth, Starting.Affiliation~~Qarth, End.of.S5~~Sons of the Harpy, End.of.S4~~Sons of the Harpy, Starting.Affiliation~~Essos Slavers, End.of.S2~~Essos Slavers, End.of.S1~~Essos Slavers, End.of.S3~~Essos Slavers, Starting.Affiliation~~Khal Drogo, End.of.S1~~Daenerys Targaryen, End.of.S2~~Daenerys Targaryen, End.of.S3~~Daenerys Targaryen, End.of.S4~~Daenerys Targaryen, End.of.S5~~Daenerys Targaryen, End.of.S7~~Daenerys Targaryen, End.of.S6~~Daenerys Targaryen, Origin~~Essos, End.of.S4~~Other, Essos, End.of.S3~~Other, Essos, End.of.S6~~Other, Essos, End.of.S7~~Other, Essos, End.of.S5~~Other, Essos, End.of.S1~~Other, Essos, End.of.S2~~Other, Essos, Starting.Affiliation~~Other, Essos, End.of.S4~~Dothraki, End.of.S1~~Dothraki, End.of.S2~~Dothraki, End.of.S3~~Dothraki, End.of.S5~~Dothraki, Starting.Affiliation~~Other Dothraki, End.of.S5~~Sand Snakes, End.of.S4~~Sand Snakes, Origin~~House Martell, Starting.Affiliation~~Viserys Targaryen, Origin~~House Targaryen, End.of.S1~~Night's Watch, Starting.Affiliation~~Night's Watch, End.of.S2~~Night's Watch, End.of.S3~~Night's Watch, End.of.S5~~Night's Watch, End.of.S6~~Night's Watch, End.of.S7~~Night's Watch, End.of.S4~~Night's Watch, End.of.S6~~Deceased, End.of.S1~~Deceased, End.of.S2~~Deceased, End.of.S3~~Deceased, End.of.S4~~Deceased, End.of.S5~~Deceased, End.of.S7~~Deceased, Origin~~House Stark, End.of.S5~~Roose Bolton, Lord Paramount of the North, End.of.S2~~Robb Stark, King in the North, End.of.S1~~Robb Stark, King in the North, Starting.Affiliation~~King Robert Baratheon, Origin~~House Tully, End.of.S3~~Other, Westeros, End.of.S5~~Other, Westeros, End.of.S7~~Other, Westeros, End.of.S6~~Other, Westeros, End.of.S4~~Other, Westeros, End.of.S2~~Other, Westeros, End.of.S1~~Other, Westeros, Starting.Affiliation~~Other, Westeros, Origin~~Westeros, End.of.S5~~High Sparrow, End.of.S3~~King Joffrey Baratheon, End.of.S4~~King Tommen Baratheon, End.of.S5~~King Tommen Baratheon, End.of.S6~~Queen Cersei Lannister, End.of.S7~~Queen Cersei Lannister, End.of.S2~~King Joffrey Baratheon, End.of.S1~~King Joffrey Baratheon, Origin~~House Lannister, Origin~~House Tyrell, End.of.S1~~King Renly Baratheon, Origin~~House Baratheon, End.of.S1~~King Stannis Baratheon, End.of.S3~~King Stannis Baratheon, End.of.S4~~King Stannis Baratheon, End.of.S2~~King Stannis Baratheon, End.of.S2~~Brotherhood Without Banners, End.of.S1~~Brotherhood Without Banners, End.of.S3~~Brotherhood Without Banners, End.of.S4~~Brotherhood Without Banners, End.of.S5~~Brotherhood Without Banners, End.of.S6~~Brotherhood Without Banners, Origin~~Wildlings, Starting.Affiliation~~Wildlings, End.of.S2~~Wildlings, End.of.S1~~Wildlings, End.of.S3~~Wildlings, End.of.S4~~Wildlings, End.of.S5~~Wildlings, End.of.S6~~Jon Snow, King in the North, End.of.S7~~Jon Snow, King in the North, Origin~~House Arryn, End.of.S4~~Petyr Baelish, Lord Protector of the Vale, End.of.S5~~Petyr Baelish, Lord Protector of the Vale, End.of.S3~~House Arryn (Neutral), End.of.S1~~House Arryn (Neutral), End.of.S2~~House Arryn (Neutral), End.of.S6~~Petyr Baelish, Lord Protector of the Vale, End.of.S7~~White Walkers, End.of.S5~~White Walkers, End.of.S6~~White Walkers, End.of.S4~~White Walkers, End.of.S3~~White Walkers, End.of.S1~~White Walkers, End.of.S2~~White Walkers, Starting.Affiliation~~White Walkers, Origin~~White Walkers## Determining optimal cycle start## Starting iteration 1 / 122## neighbornet_objective for iteration 1 = 63295## Starting iteration 2 / 122## neighbornet_objective for iteration 2 = 68534## Starting iteration 3 / 122## neighbornet_objective for iteration 3 = 66798## Starting iteration 4 / 122## neighbornet_objective for iteration 4 = 67281## Starting iteration 5 / 122## neighbornet_objective for iteration 5 = 66553## Starting iteration 6 / 122## neighbornet_objective for iteration 6 = 66551## Starting iteration 7 / 122## neighbornet_objective for iteration 7 = 66090## Starting iteration 8 / 122## neighbornet_objective for iteration 8 = 62905## Starting iteration 9 / 122## neighbornet_objective for iteration 9 = 63194## Starting iteration 10 / 122## neighbornet_objective for iteration 10 = 64032## Starting iteration 11 / 122## neighbornet_objective for iteration 11 = 67407## Starting iteration 12 / 122## neighbornet_objective for iteration 12 = 66705## Starting iteration 13 / 122## neighbornet_objective for iteration 13 = 79919## Starting iteration 14 / 122## neighbornet_objective for iteration 14 = 79697## Starting iteration 15 / 122## neighbornet_objective for iteration 15 = 112217## Starting iteration 16 / 122## neighbornet_objective for iteration 16 = 146183## Starting iteration 17 / 122## neighbornet_objective for iteration 17 = 110984## Starting iteration 18 / 122## neighbornet_objective for iteration 18 = 101749## Starting iteration 19 / 122## neighbornet_objective for iteration 19 = 110091## Starting iteration 20 / 122## neighbornet_objective for iteration 20 = 115804## Starting iteration 21 / 122## neighbornet_objective for iteration 21 = 115704## Starting iteration 22 / 122## neighbornet_objective for iteration 22 = 113139## Starting iteration 23 / 122## neighbornet_objective for iteration 23 = 110998## Starting iteration 24 / 122## neighbornet_objective for iteration 24 = 110835## Starting iteration 25 / 122## neighbornet_objective for iteration 25 = 134620## Starting iteration 26 / 122## neighbornet_objective for iteration 26 = 99946## Starting iteration 27 / 122## neighbornet_objective for iteration 27 = 102125## Starting iteration 28 / 122## neighbornet_objective for iteration 28 = 128064## Starting iteration 29 / 122## neighbornet_objective for iteration 29 = 126205## Starting iteration 30 / 122## neighbornet_objective for iteration 30 = 151220## Starting iteration 31 / 122## neighbornet_objective for iteration 31 = 138340## Starting iteration 32 / 122## neighbornet_objective for iteration 32 = 110898## Starting iteration 33 / 122## neighbornet_objective for iteration 33 = 140859## Starting iteration 34 / 122## neighbornet_objective for iteration 34 = 112500## Starting iteration 35 / 122## neighbornet_objective for iteration 35 = 80640## Starting iteration 36 / 122## neighbornet_objective for iteration 36 = 99360## Starting iteration 37 / 122## neighbornet_objective for iteration 37 = 116358## Starting iteration 38 / 122## neighbornet_objective for iteration 38 = 116358## Starting iteration 39 / 122## neighbornet_objective for iteration 39 = 97638## Starting iteration 40 / 122## neighbornet_objective for iteration 40 = 84472## Starting iteration 41 / 122## neighbornet_objective for iteration 41 = 69510## Starting iteration 42 / 122## neighbornet_objective for iteration 42 = 70146## Starting iteration 43 / 122## neighbornet_objective for iteration 43 = 68182## Starting iteration 44 / 122## neighbornet_objective for iteration 44 = 69569## Starting iteration 45 / 122## neighbornet_objective for iteration 45 = 69871## Starting iteration 46 / 122## neighbornet_objective for iteration 46 = 70198## Starting iteration 47 / 122## neighbornet_objective for iteration 47 = 103703## Starting iteration 48 / 122## neighbornet_objective for iteration 48 = 93752## Starting iteration 49 / 122## neighbornet_objective for iteration 49 = 93940## Starting iteration 50 / 122## neighbornet_objective for iteration 50 = 93599## Starting iteration 51 / 122## neighbornet_objective for iteration 51 = 114908## Starting iteration 52 / 122## neighbornet_objective for iteration 52 = 114986## Starting iteration 53 / 122## neighbornet_objective for iteration 53 = 105626## Starting iteration 54 / 122## neighbornet_objective for iteration 54 = 83368## Starting iteration 55 / 122## neighbornet_objective for iteration 55 = 182857## Starting iteration 56 / 122## neighbornet_objective for iteration 56 = 195335## Starting iteration 57 / 122## neighbornet_objective for iteration 57 = 207356## Starting iteration 58 / 122## neighbornet_objective for iteration 58 = 217970## Starting iteration 59 / 122## neighbornet_objective for iteration 59 = 228331## Starting iteration 60 / 122## neighbornet_objective for iteration 60 = 147868## Starting iteration 61 / 122## neighbornet_objective for iteration 61 = 93244## Starting iteration 62 / 122## neighbornet_objective for iteration 62 = 113646## Starting iteration 63 / 122## neighbornet_objective for iteration 63 = 117666## Starting iteration 64 / 122## neighbornet_objective for iteration 64 = 156784## Starting iteration 65 / 122## neighbornet_objective for iteration 65 = 142976## Starting iteration 66 / 122## neighbornet_objective for iteration 66 = 130734## Starting iteration 67 / 122## neighbornet_objective for iteration 67 = 121388## Starting iteration 68 / 122## neighbornet_objective for iteration 68 = 165681## Starting iteration 69 / 122## neighbornet_objective for iteration 69 = 217180## Starting iteration 70 / 122## neighbornet_objective for iteration 70 = 238589## Starting iteration 71 / 122## neighbornet_objective for iteration 71 = 197797## Starting iteration 72 / 122## neighbornet_objective for iteration 72 = 141234## Starting iteration 73 / 122## neighbornet_objective for iteration 73 = 140881## Starting iteration 74 / 122## neighbornet_objective for iteration 74 = 139249## Starting iteration 75 / 122## neighbornet_objective for iteration 75 = 142303## Starting iteration 76 / 122## neighbornet_objective for iteration 76 = 118698## Starting iteration 77 / 122## neighbornet_objective for iteration 77 = 118629## Starting iteration 78 / 122## neighbornet_objective for iteration 78 = 169162## Starting iteration 79 / 122## neighbornet_objective for iteration 79 = 151874## Starting iteration 80 / 122## neighbornet_objective for iteration 80 = 144722## Starting iteration 81 / 122## neighbornet_objective for iteration 81 = 140100## Starting iteration 82 / 122## neighbornet_objective for iteration 82 = 126308## Starting iteration 83 / 122## neighbornet_objective for iteration 83 = 127234## Starting iteration 84 / 122## neighbornet_objective for iteration 84 = 94873## Starting iteration 85 / 122## neighbornet_objective for iteration 85 = 86574## Starting iteration 86 / 122## neighbornet_objective for iteration 86 = 86605## Starting iteration 87 / 122## neighbornet_objective for iteration 87 = 80011## Starting iteration 88 / 122## neighbornet_objective for iteration 88 = 73857## Starting iteration 89 / 122## neighbornet_objective for iteration 89 = 74761## Starting iteration 90 / 122## neighbornet_objective for iteration 90 = 80906## Starting iteration 91 / 122## neighbornet_objective for iteration 91 = 78858## Starting iteration 92 / 122## neighbornet_objective for iteration 92 = 72448## Starting iteration 93 / 122## neighbornet_objective for iteration 93 = 81070## Starting iteration 94 / 122## neighbornet_objective for iteration 94 = 73675## Starting iteration 95 / 122## neighbornet_objective for iteration 95 = 73675## Starting iteration 96 / 122## neighbornet_objective for iteration 96 = 73661## Starting iteration 97 / 122## neighbornet_objective for iteration 97 = 71056## Starting iteration 98 / 122## neighbornet_objective for iteration 98 = 71003## Starting iteration 99 / 122## neighbornet_objective for iteration 99 = 89315## Starting iteration 100 / 122## neighbornet_objective for iteration 100 = 88889## Starting iteration 101 / 122## neighbornet_objective for iteration 101 = 115981## Starting iteration 102 / 122## neighbornet_objective for iteration 102 = 84167## Starting iteration 103 / 122## neighbornet_objective for iteration 103 = 83081## Starting iteration 104 / 122## neighbornet_objective for iteration 104 = 81046## Starting iteration 105 / 122## neighbornet_objective for iteration 105 = 78241## Starting iteration 106 / 122## neighbornet_objective for iteration 106 = 86713## Starting iteration 107 / 122## neighbornet_objective for iteration 107 = 73531## Starting iteration 108 / 122## neighbornet_objective for iteration 108 = 75541## Starting iteration 109 / 122## neighbornet_objective for iteration 109 = 85664## Starting iteration 110 / 122## neighbornet_objective for iteration 110 = 83844## Starting iteration 111 / 122## neighbornet_objective for iteration 111 = 84380## Starting iteration 112 / 122## neighbornet_objective for iteration 112 = 86888## Starting iteration 113 / 122## neighbornet_objective for iteration 113 = 75464## Starting iteration 114 / 122## neighbornet_objective for iteration 114 = 66759## Starting iteration 115 / 122## neighbornet_objective for iteration 115 = 75147## Starting iteration 116 / 122## neighbornet_objective for iteration 116 = 85767## Starting iteration 117 / 122## neighbornet_objective for iteration 117 = 69770## Starting iteration 118 / 122## neighbornet_objective for iteration 118 = 69836## Starting iteration 119 / 122## neighbornet_objective for iteration 119 = 69567## Starting iteration 120 / 122## neighbornet_objective for iteration 120 = 77881## Starting iteration 121 / 122## neighbornet_objective for iteration 121 = 68331## Starting iteration 122 / 122## neighbornet_objective for iteration 122 = 65695## crossing edges objective = 62905## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/GoT_NN.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
# df['Lannister_origin'] <- df["Origin"] == 'House Lannister'
df <- df %>%
mutate(Lannister_origin = case_when(
Origin == "House Lannister" ~ 1,
Origin == "Westeros" ~ 2,
TRUE ~ 0
))
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "neighbornet", optimize_column_order = FALSE, color_boxes = FALSE, color_bands = TRUE, min_text = 0.01, verbose = TRUE, color_band_list = c("0" = "grey", "1" = "#D55E00", "2" = "#56B4E9"), color_band_column = "Lannister_origin", output_plot_path = file.path(output_dir, "GoT_NN_lannister_origin.pdf"), save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 488## m = 9## a = 183## k_1 = 13## k_2 = 12## k_3 = 17## k_4 = 16## k_5 = 14## k_6 = 15## k_7 = 16## k_8 = 11## k_9 = 8## K_sum = 122## K_prod = 12546293760## Sorting data with sorting_algorithm=neighbornet## Running neighbornet## Running 'neighbornet' for stratum order## Cycle: End.of.S1~~Balon Greyjoy, King of the Iron Islands, Origin~~House Greyjoy, End.of.S2~~Balon Greyjoy, King of the Iron Islands, End.of.S3~~Balon Greyjoy, King of the Iron Islands, End.of.S4~~Balon Greyjoy, King of the Iron Islands, End.of.S5~~Balon Greyjoy, King of the Iron Islands, End.of.S6~~Euron Greyjoy, King of the Iron Islands, Starting.Affiliation~~Deceased, End.of.S2~~Qarth, End.of.S1~~Qarth, Starting.Affiliation~~Qarth, End.of.S5~~Sons of the Harpy, End.of.S4~~Sons of the Harpy, Starting.Affiliation~~Essos Slavers, End.of.S2~~Essos Slavers, End.of.S1~~Essos Slavers, End.of.S3~~Essos Slavers, Starting.Affiliation~~Khal Drogo, End.of.S1~~Daenerys Targaryen, End.of.S2~~Daenerys Targaryen, End.of.S3~~Daenerys Targaryen, End.of.S4~~Daenerys Targaryen, End.of.S5~~Daenerys Targaryen, End.of.S7~~Daenerys Targaryen, End.of.S6~~Daenerys Targaryen, Origin~~Essos, End.of.S4~~Other, Essos, End.of.S3~~Other, Essos, End.of.S6~~Other, Essos, End.of.S7~~Other, Essos, End.of.S5~~Other, Essos, End.of.S1~~Other, Essos, End.of.S2~~Other, Essos, Starting.Affiliation~~Other, Essos, End.of.S4~~Dothraki, End.of.S1~~Dothraki, End.of.S2~~Dothraki, End.of.S3~~Dothraki, End.of.S5~~Dothraki, Starting.Affiliation~~Other Dothraki, End.of.S5~~Sand Snakes, End.of.S4~~Sand Snakes, Origin~~House Martell, Starting.Affiliation~~Viserys Targaryen, Origin~~House Targaryen, End.of.S1~~Night's Watch, Starting.Affiliation~~Night's Watch, End.of.S2~~Night's Watch, End.of.S3~~Night's Watch, End.of.S5~~Night's Watch, End.of.S6~~Night's Watch, End.of.S7~~Night's Watch, End.of.S4~~Night's Watch, End.of.S6~~Deceased, End.of.S1~~Deceased, End.of.S2~~Deceased, End.of.S3~~Deceased, End.of.S4~~Deceased, End.of.S5~~Deceased, End.of.S7~~Deceased, Origin~~House Stark, End.of.S5~~Roose Bolton, Lord Paramount of the North, End.of.S2~~Robb Stark, King in the North, End.of.S1~~Robb Stark, King in the North, Starting.Affiliation~~King Robert Baratheon, Origin~~House Tully, End.of.S3~~Other, Westeros, End.of.S5~~Other, Westeros, End.of.S7~~Other, Westeros, End.of.S6~~Other, Westeros, End.of.S4~~Other, Westeros, End.of.S2~~Other, Westeros, End.of.S1~~Other, Westeros, Starting.Affiliation~~Other, Westeros, Origin~~Westeros, End.of.S5~~High Sparrow, End.of.S3~~King Joffrey Baratheon, End.of.S4~~King Tommen Baratheon, End.of.S5~~King Tommen Baratheon, End.of.S6~~Queen Cersei Lannister, End.of.S7~~Queen Cersei Lannister, End.of.S2~~King Joffrey Baratheon, End.of.S1~~King Joffrey Baratheon, Origin~~House Lannister, Origin~~House Tyrell, End.of.S1~~King Renly Baratheon, Origin~~House Baratheon, End.of.S1~~King Stannis Baratheon, End.of.S3~~King Stannis Baratheon, End.of.S4~~King Stannis Baratheon, End.of.S2~~King Stannis Baratheon, End.of.S2~~Brotherhood Without Banners, End.of.S1~~Brotherhood Without Banners, End.of.S3~~Brotherhood Without Banners, End.of.S4~~Brotherhood Without Banners, End.of.S5~~Brotherhood Without Banners, End.of.S6~~Brotherhood Without Banners, Origin~~Wildlings, Starting.Affiliation~~Wildlings, End.of.S2~~Wildlings, End.of.S1~~Wildlings, End.of.S3~~Wildlings, End.of.S4~~Wildlings, End.of.S5~~Wildlings, End.of.S6~~Jon Snow, King in the North, End.of.S7~~Jon Snow, King in the North, Origin~~House Arryn, End.of.S4~~Petyr Baelish, Lord Protector of the Vale, End.of.S5~~Petyr Baelish, Lord Protector of the Vale, End.of.S3~~House Arryn (Neutral), End.of.S1~~House Arryn (Neutral), End.of.S2~~House Arryn (Neutral), End.of.S6~~Petyr Baelish, Lord Protector of the Vale, End.of.S7~~White Walkers, End.of.S5~~White Walkers, End.of.S6~~White Walkers, End.of.S4~~White Walkers, End.of.S3~~White Walkers, End.of.S1~~White Walkers, End.of.S2~~White Walkers, Starting.Affiliation~~White Walkers, Origin~~White Walkers## Determining optimal cycle start## Starting iteration 1 / 122## neighbornet_objective for iteration 1 = 63295## Starting iteration 2 / 122## neighbornet_objective for iteration 2 = 68534## Starting iteration 3 / 122## neighbornet_objective for iteration 3 = 66798## Starting iteration 4 / 122## neighbornet_objective for iteration 4 = 67281## Starting iteration 5 / 122## neighbornet_objective for iteration 5 = 66553## Starting iteration 6 / 122## neighbornet_objective for iteration 6 = 66551## Starting iteration 7 / 122## neighbornet_objective for iteration 7 = 66090## Starting iteration 8 / 122## neighbornet_objective for iteration 8 = 62905## Starting iteration 9 / 122## neighbornet_objective for iteration 9 = 63194## Starting iteration 10 / 122## neighbornet_objective for iteration 10 = 64032## Starting iteration 11 / 122## neighbornet_objective for iteration 11 = 67407## Starting iteration 12 / 122## neighbornet_objective for iteration 12 = 66705## Starting iteration 13 / 122## neighbornet_objective for iteration 13 = 79919## Starting iteration 14 / 122## neighbornet_objective for iteration 14 = 79697## Starting iteration 15 / 122## neighbornet_objective for iteration 15 = 112217## Starting iteration 16 / 122## neighbornet_objective for iteration 16 = 146183## Starting iteration 17 / 122## neighbornet_objective for iteration 17 = 110984## Starting iteration 18 / 122## neighbornet_objective for iteration 18 = 101749## Starting iteration 19 / 122## neighbornet_objective for iteration 19 = 110091## Starting iteration 20 / 122## neighbornet_objective for iteration 20 = 115804## Starting iteration 21 / 122## neighbornet_objective for iteration 21 = 115704## Starting iteration 22 / 122## neighbornet_objective for iteration 22 = 113139## Starting iteration 23 / 122## neighbornet_objective for iteration 23 = 110998## Starting iteration 24 / 122## neighbornet_objective for iteration 24 = 110835## Starting iteration 25 / 122## neighbornet_objective for iteration 25 = 134620## Starting iteration 26 / 122## neighbornet_objective for iteration 26 = 99946## Starting iteration 27 / 122## neighbornet_objective for iteration 27 = 102125## Starting iteration 28 / 122## neighbornet_objective for iteration 28 = 128064## Starting iteration 29 / 122## neighbornet_objective for iteration 29 = 126205## Starting iteration 30 / 122## neighbornet_objective for iteration 30 = 151220## Starting iteration 31 / 122## neighbornet_objective for iteration 31 = 138340## Starting iteration 32 / 122## neighbornet_objective for iteration 32 = 110898## Starting iteration 33 / 122## neighbornet_objective for iteration 33 = 140859## Starting iteration 34 / 122## neighbornet_objective for iteration 34 = 112500## Starting iteration 35 / 122## neighbornet_objective for iteration 35 = 80640## Starting iteration 36 / 122## neighbornet_objective for iteration 36 = 99360## Starting iteration 37 / 122## neighbornet_objective for iteration 37 = 116358## Starting iteration 38 / 122## neighbornet_objective for iteration 38 = 116358## Starting iteration 39 / 122## neighbornet_objective for iteration 39 = 97638## Starting iteration 40 / 122## neighbornet_objective for iteration 40 = 84472## Starting iteration 41 / 122## neighbornet_objective for iteration 41 = 69510## Starting iteration 42 / 122## neighbornet_objective for iteration 42 = 70146## Starting iteration 43 / 122## neighbornet_objective for iteration 43 = 68182## Starting iteration 44 / 122## neighbornet_objective for iteration 44 = 69569## Starting iteration 45 / 122## neighbornet_objective for iteration 45 = 69871## Starting iteration 46 / 122## neighbornet_objective for iteration 46 = 70198## Starting iteration 47 / 122## neighbornet_objective for iteration 47 = 103703## Starting iteration 48 / 122## neighbornet_objective for iteration 48 = 93752## Starting iteration 49 / 122## neighbornet_objective for iteration 49 = 93940## Starting iteration 50 / 122## neighbornet_objective for iteration 50 = 93599## Starting iteration 51 / 122## neighbornet_objective for iteration 51 = 114908## Starting iteration 52 / 122## neighbornet_objective for iteration 52 = 114986## Starting iteration 53 / 122## neighbornet_objective for iteration 53 = 105626## Starting iteration 54 / 122## neighbornet_objective for iteration 54 = 83368## Starting iteration 55 / 122## neighbornet_objective for iteration 55 = 182857## Starting iteration 56 / 122## neighbornet_objective for iteration 56 = 195335## Starting iteration 57 / 122## neighbornet_objective for iteration 57 = 207356## Starting iteration 58 / 122## neighbornet_objective for iteration 58 = 217970## Starting iteration 59 / 122## neighbornet_objective for iteration 59 = 228331## Starting iteration 60 / 122## neighbornet_objective for iteration 60 = 147868## Starting iteration 61 / 122## neighbornet_objective for iteration 61 = 93244## Starting iteration 62 / 122## neighbornet_objective for iteration 62 = 113646## Starting iteration 63 / 122## neighbornet_objective for iteration 63 = 117666## Starting iteration 64 / 122## neighbornet_objective for iteration 64 = 156784## Starting iteration 65 / 122## neighbornet_objective for iteration 65 = 142976## Starting iteration 66 / 122## neighbornet_objective for iteration 66 = 130734## Starting iteration 67 / 122## neighbornet_objective for iteration 67 = 121388## Starting iteration 68 / 122## neighbornet_objective for iteration 68 = 165681## Starting iteration 69 / 122## neighbornet_objective for iteration 69 = 217180## Starting iteration 70 / 122## neighbornet_objective for iteration 70 = 238589## Starting iteration 71 / 122## neighbornet_objective for iteration 71 = 197797## Starting iteration 72 / 122## neighbornet_objective for iteration 72 = 141234## Starting iteration 73 / 122## neighbornet_objective for iteration 73 = 140881## Starting iteration 74 / 122## neighbornet_objective for iteration 74 = 139249## Starting iteration 75 / 122## neighbornet_objective for iteration 75 = 142303## Starting iteration 76 / 122## neighbornet_objective for iteration 76 = 118698## Starting iteration 77 / 122## neighbornet_objective for iteration 77 = 118629## Starting iteration 78 / 122## neighbornet_objective for iteration 78 = 169162## Starting iteration 79 / 122## neighbornet_objective for iteration 79 = 151874## Starting iteration 80 / 122## neighbornet_objective for iteration 80 = 144722## Starting iteration 81 / 122## neighbornet_objective for iteration 81 = 140100## Starting iteration 82 / 122## neighbornet_objective for iteration 82 = 126308## Starting iteration 83 / 122## neighbornet_objective for iteration 83 = 127234## Starting iteration 84 / 122## neighbornet_objective for iteration 84 = 94873## Starting iteration 85 / 122## neighbornet_objective for iteration 85 = 86574## Starting iteration 86 / 122## neighbornet_objective for iteration 86 = 86605## Starting iteration 87 / 122## neighbornet_objective for iteration 87 = 80011## Starting iteration 88 / 122## neighbornet_objective for iteration 88 = 73857## Starting iteration 89 / 122## neighbornet_objective for iteration 89 = 74761## Starting iteration 90 / 122## neighbornet_objective for iteration 90 = 80906## Starting iteration 91 / 122## neighbornet_objective for iteration 91 = 78858## Starting iteration 92 / 122## neighbornet_objective for iteration 92 = 72448## Starting iteration 93 / 122## neighbornet_objective for iteration 93 = 81070## Starting iteration 94 / 122## neighbornet_objective for iteration 94 = 73675## Starting iteration 95 / 122## neighbornet_objective for iteration 95 = 73675## Starting iteration 96 / 122## neighbornet_objective for iteration 96 = 73661## Starting iteration 97 / 122## neighbornet_objective for iteration 97 = 71056## Starting iteration 98 / 122## neighbornet_objective for iteration 98 = 71003## Starting iteration 99 / 122## neighbornet_objective for iteration 99 = 89315## Starting iteration 100 / 122## neighbornet_objective for iteration 100 = 88889## Starting iteration 101 / 122## neighbornet_objective for iteration 101 = 115981## Starting iteration 102 / 122## neighbornet_objective for iteration 102 = 84167## Starting iteration 103 / 122## neighbornet_objective for iteration 103 = 83081## Starting iteration 104 / 122## neighbornet_objective for iteration 104 = 81046## Starting iteration 105 / 122## neighbornet_objective for iteration 105 = 78241## Starting iteration 106 / 122## neighbornet_objective for iteration 106 = 86713## Starting iteration 107 / 122## neighbornet_objective for iteration 107 = 73531## Starting iteration 108 / 122## neighbornet_objective for iteration 108 = 75541## Starting iteration 109 / 122## neighbornet_objective for iteration 109 = 85664## Starting iteration 110 / 122## neighbornet_objective for iteration 110 = 83844## Starting iteration 111 / 122## neighbornet_objective for iteration 111 = 84380## Starting iteration 112 / 122## neighbornet_objective for iteration 112 = 86888## Starting iteration 113 / 122## neighbornet_objective for iteration 113 = 75464## Starting iteration 114 / 122## neighbornet_objective for iteration 114 = 66759## Starting iteration 115 / 122## neighbornet_objective for iteration 115 = 75147## Starting iteration 116 / 122## neighbornet_objective for iteration 116 = 85767## Starting iteration 117 / 122## neighbornet_objective for iteration 117 = 69770## Starting iteration 118 / 122## neighbornet_objective for iteration 118 = 69836## Starting iteration 119 / 122## neighbornet_objective for iteration 119 = 69567## Starting iteration 120 / 122## neighbornet_objective for iteration 120 = 77881## Starting iteration 121 / 122## neighbornet_objective for iteration 121 = 68331## Starting iteration 122 / 122## neighbornet_objective for iteration 122 = 65695## crossing edges objective = 62905## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/GoT_NN_lannister_origin.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
# df['Lannister_ending'] <- df["End.of.S7"] == 'Queen Cersei Lannister'
df <- df %>%
mutate(Lannister_ending = case_when(
End.of.S7 == "Queen Cersei Lannister" ~ 1,
End.of.S7 == "Other, Westeros" ~ 2,
TRUE ~ 0
))
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "neighbornet", optimize_column_order = FALSE, color_boxes = FALSE, color_bands = TRUE, min_text = 0.01, verbose = TRUE, color_band_list = c("0" = "grey", "1" = "#D55E00", "2" = "#56B4E9"), color_band_column = "Lannister_ending", output_plot_path = file.path(output_dir, "GoT_NN_lannister_ending.pdf"), save_height = 24, save_width = 24, text_size = 35, axis_text_size = 25, axis_text_vjust = -5, text_width = 0.5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 488## m = 9## a = 183## k_1 = 13## k_2 = 12## k_3 = 17## k_4 = 16## k_5 = 14## k_6 = 15## k_7 = 16## k_8 = 11## k_9 = 8## K_sum = 122## K_prod = 12546293760## Sorting data with sorting_algorithm=neighbornet## Running neighbornet## Running 'neighbornet' for stratum order## Cycle: End.of.S1~~Balon Greyjoy, King of the Iron Islands, Origin~~House Greyjoy, End.of.S2~~Balon Greyjoy, King of the Iron Islands, End.of.S3~~Balon Greyjoy, King of the Iron Islands, End.of.S4~~Balon Greyjoy, King of the Iron Islands, End.of.S5~~Balon Greyjoy, King of the Iron Islands, End.of.S6~~Euron Greyjoy, King of the Iron Islands, Starting.Affiliation~~Deceased, End.of.S2~~Qarth, End.of.S1~~Qarth, Starting.Affiliation~~Qarth, End.of.S5~~Sons of the Harpy, End.of.S4~~Sons of the Harpy, Starting.Affiliation~~Essos Slavers, End.of.S2~~Essos Slavers, End.of.S1~~Essos Slavers, End.of.S3~~Essos Slavers, Starting.Affiliation~~Khal Drogo, End.of.S1~~Daenerys Targaryen, End.of.S2~~Daenerys Targaryen, End.of.S3~~Daenerys Targaryen, End.of.S4~~Daenerys Targaryen, End.of.S5~~Daenerys Targaryen, End.of.S7~~Daenerys Targaryen, End.of.S6~~Daenerys Targaryen, Origin~~Essos, End.of.S4~~Other, Essos, End.of.S3~~Other, Essos, End.of.S6~~Other, Essos, End.of.S7~~Other, Essos, End.of.S5~~Other, Essos, End.of.S1~~Other, Essos, End.of.S2~~Other, Essos, Starting.Affiliation~~Other, Essos, End.of.S4~~Dothraki, End.of.S1~~Dothraki, End.of.S2~~Dothraki, End.of.S3~~Dothraki, End.of.S5~~Dothraki, Starting.Affiliation~~Other Dothraki, End.of.S5~~Sand Snakes, End.of.S4~~Sand Snakes, Origin~~House Martell, Starting.Affiliation~~Viserys Targaryen, Origin~~House Targaryen, End.of.S1~~Night's Watch, Starting.Affiliation~~Night's Watch, End.of.S2~~Night's Watch, End.of.S3~~Night's Watch, End.of.S5~~Night's Watch, End.of.S6~~Night's Watch, End.of.S7~~Night's Watch, End.of.S4~~Night's Watch, End.of.S6~~Deceased, End.of.S1~~Deceased, End.of.S2~~Deceased, End.of.S3~~Deceased, End.of.S4~~Deceased, End.of.S5~~Deceased, End.of.S7~~Deceased, Origin~~House Stark, End.of.S5~~Roose Bolton, Lord Paramount of the North, End.of.S2~~Robb Stark, King in the North, End.of.S1~~Robb Stark, King in the North, Starting.Affiliation~~King Robert Baratheon, Origin~~House Tully, End.of.S3~~Other, Westeros, End.of.S5~~Other, Westeros, End.of.S7~~Other, Westeros, End.of.S6~~Other, Westeros, End.of.S4~~Other, Westeros, End.of.S2~~Other, Westeros, End.of.S1~~Other, Westeros, Starting.Affiliation~~Other, Westeros, Origin~~Westeros, End.of.S5~~High Sparrow, End.of.S3~~King Joffrey Baratheon, End.of.S4~~King Tommen Baratheon, End.of.S5~~King Tommen Baratheon, End.of.S6~~Queen Cersei Lannister, End.of.S7~~Queen Cersei Lannister, End.of.S2~~King Joffrey Baratheon, End.of.S1~~King Joffrey Baratheon, Origin~~House Lannister, Origin~~House Tyrell, End.of.S1~~King Renly Baratheon, Origin~~House Baratheon, End.of.S1~~King Stannis Baratheon, End.of.S3~~King Stannis Baratheon, End.of.S4~~King Stannis Baratheon, End.of.S2~~King Stannis Baratheon, End.of.S2~~Brotherhood Without Banners, End.of.S1~~Brotherhood Without Banners, End.of.S3~~Brotherhood Without Banners, End.of.S4~~Brotherhood Without Banners, End.of.S5~~Brotherhood Without Banners, End.of.S6~~Brotherhood Without Banners, Origin~~Wildlings, Starting.Affiliation~~Wildlings, End.of.S2~~Wildlings, End.of.S1~~Wildlings, End.of.S3~~Wildlings, End.of.S4~~Wildlings, End.of.S5~~Wildlings, End.of.S6~~Jon Snow, King in the North, End.of.S7~~Jon Snow, King in the North, Origin~~House Arryn, End.of.S4~~Petyr Baelish, Lord Protector of the Vale, End.of.S5~~Petyr Baelish, Lord Protector of the Vale, End.of.S3~~House Arryn (Neutral), End.of.S1~~House Arryn (Neutral), End.of.S2~~House Arryn (Neutral), End.of.S6~~Petyr Baelish, Lord Protector of the Vale, End.of.S7~~White Walkers, End.of.S5~~White Walkers, End.of.S6~~White Walkers, End.of.S4~~White Walkers, End.of.S3~~White Walkers, End.of.S1~~White Walkers, End.of.S2~~White Walkers, Starting.Affiliation~~White Walkers, Origin~~White Walkers## Determining optimal cycle start## Starting iteration 1 / 122## neighbornet_objective for iteration 1 = 63295## Starting iteration 2 / 122## neighbornet_objective for iteration 2 = 68534## Starting iteration 3 / 122## neighbornet_objective for iteration 3 = 66798## Starting iteration 4 / 122## neighbornet_objective for iteration 4 = 67281## Starting iteration 5 / 122## neighbornet_objective for iteration 5 = 66553## Starting iteration 6 / 122## neighbornet_objective for iteration 6 = 66551## Starting iteration 7 / 122## neighbornet_objective for iteration 7 = 66090## Starting iteration 8 / 122## neighbornet_objective for iteration 8 = 62905## Starting iteration 9 / 122## neighbornet_objective for iteration 9 = 63194## Starting iteration 10 / 122## neighbornet_objective for iteration 10 = 64032## Starting iteration 11 / 122## neighbornet_objective for iteration 11 = 67407## Starting iteration 12 / 122## neighbornet_objective for iteration 12 = 66705## Starting iteration 13 / 122## neighbornet_objective for iteration 13 = 79919## Starting iteration 14 / 122## neighbornet_objective for iteration 14 = 79697## Starting iteration 15 / 122## neighbornet_objective for iteration 15 = 112217## Starting iteration 16 / 122## neighbornet_objective for iteration 16 = 146183## Starting iteration 17 / 122## neighbornet_objective for iteration 17 = 110984## Starting iteration 18 / 122## neighbornet_objective for iteration 18 = 101749## Starting iteration 19 / 122## neighbornet_objective for iteration 19 = 110091## Starting iteration 20 / 122## neighbornet_objective for iteration 20 = 115804## Starting iteration 21 / 122## neighbornet_objective for iteration 21 = 115704## Starting iteration 22 / 122## neighbornet_objective for iteration 22 = 113139## Starting iteration 23 / 122## neighbornet_objective for iteration 23 = 110998## Starting iteration 24 / 122## neighbornet_objective for iteration 24 = 110835## Starting iteration 25 / 122## neighbornet_objective for iteration 25 = 134620## Starting iteration 26 / 122## neighbornet_objective for iteration 26 = 99946## Starting iteration 27 / 122## neighbornet_objective for iteration 27 = 102125## Starting iteration 28 / 122## neighbornet_objective for iteration 28 = 128064## Starting iteration 29 / 122## neighbornet_objective for iteration 29 = 126205## Starting iteration 30 / 122## neighbornet_objective for iteration 30 = 151220## Starting iteration 31 / 122## neighbornet_objective for iteration 31 = 138340## Starting iteration 32 / 122## neighbornet_objective for iteration 32 = 110898## Starting iteration 33 / 122## neighbornet_objective for iteration 33 = 140859## Starting iteration 34 / 122## neighbornet_objective for iteration 34 = 112500## Starting iteration 35 / 122## neighbornet_objective for iteration 35 = 80640## Starting iteration 36 / 122## neighbornet_objective for iteration 36 = 99360## Starting iteration 37 / 122## neighbornet_objective for iteration 37 = 116358## Starting iteration 38 / 122## neighbornet_objective for iteration 38 = 116358## Starting iteration 39 / 122## neighbornet_objective for iteration 39 = 97638## Starting iteration 40 / 122## neighbornet_objective for iteration 40 = 84472## Starting iteration 41 / 122## neighbornet_objective for iteration 41 = 69510## Starting iteration 42 / 122## neighbornet_objective for iteration 42 = 70146## Starting iteration 43 / 122## neighbornet_objective for iteration 43 = 68182## Starting iteration 44 / 122## neighbornet_objective for iteration 44 = 69569## Starting iteration 45 / 122## neighbornet_objective for iteration 45 = 69871## Starting iteration 46 / 122## neighbornet_objective for iteration 46 = 70198## Starting iteration 47 / 122## neighbornet_objective for iteration 47 = 103703## Starting iteration 48 / 122## neighbornet_objective for iteration 48 = 93752## Starting iteration 49 / 122## neighbornet_objective for iteration 49 = 93940## Starting iteration 50 / 122## neighbornet_objective for iteration 50 = 93599## Starting iteration 51 / 122## neighbornet_objective for iteration 51 = 114908## Starting iteration 52 / 122## neighbornet_objective for iteration 52 = 114986## Starting iteration 53 / 122## neighbornet_objective for iteration 53 = 105626## Starting iteration 54 / 122## neighbornet_objective for iteration 54 = 83368## Starting iteration 55 / 122## neighbornet_objective for iteration 55 = 182857## Starting iteration 56 / 122## neighbornet_objective for iteration 56 = 195335## Starting iteration 57 / 122## neighbornet_objective for iteration 57 = 207356## Starting iteration 58 / 122## neighbornet_objective for iteration 58 = 217970## Starting iteration 59 / 122## neighbornet_objective for iteration 59 = 228331## Starting iteration 60 / 122## neighbornet_objective for iteration 60 = 147868## Starting iteration 61 / 122## neighbornet_objective for iteration 61 = 93244## Starting iteration 62 / 122## neighbornet_objective for iteration 62 = 113646## Starting iteration 63 / 122## neighbornet_objective for iteration 63 = 117666## Starting iteration 64 / 122## neighbornet_objective for iteration 64 = 156784## Starting iteration 65 / 122## neighbornet_objective for iteration 65 = 142976## Starting iteration 66 / 122## neighbornet_objective for iteration 66 = 130734## Starting iteration 67 / 122## neighbornet_objective for iteration 67 = 121388## Starting iteration 68 / 122## neighbornet_objective for iteration 68 = 165681## Starting iteration 69 / 122## neighbornet_objective for iteration 69 = 217180## Starting iteration 70 / 122## neighbornet_objective for iteration 70 = 238589## Starting iteration 71 / 122## neighbornet_objective for iteration 71 = 197797## Starting iteration 72 / 122## neighbornet_objective for iteration 72 = 141234## Starting iteration 73 / 122## neighbornet_objective for iteration 73 = 140881## Starting iteration 74 / 122## neighbornet_objective for iteration 74 = 139249## Starting iteration 75 / 122## neighbornet_objective for iteration 75 = 142303## Starting iteration 76 / 122## neighbornet_objective for iteration 76 = 118698## Starting iteration 77 / 122## neighbornet_objective for iteration 77 = 118629## Starting iteration 78 / 122## neighbornet_objective for iteration 78 = 169162## Starting iteration 79 / 122## neighbornet_objective for iteration 79 = 151874## Starting iteration 80 / 122## neighbornet_objective for iteration 80 = 144722## Starting iteration 81 / 122## neighbornet_objective for iteration 81 = 140100## Starting iteration 82 / 122## neighbornet_objective for iteration 82 = 126308## Starting iteration 83 / 122## neighbornet_objective for iteration 83 = 127234## Starting iteration 84 / 122## neighbornet_objective for iteration 84 = 94873## Starting iteration 85 / 122## neighbornet_objective for iteration 85 = 86574## Starting iteration 86 / 122## neighbornet_objective for iteration 86 = 86605## Starting iteration 87 / 122## neighbornet_objective for iteration 87 = 80011## Starting iteration 88 / 122## neighbornet_objective for iteration 88 = 73857## Starting iteration 89 / 122## neighbornet_objective for iteration 89 = 74761## Starting iteration 90 / 122## neighbornet_objective for iteration 90 = 80906## Starting iteration 91 / 122## neighbornet_objective for iteration 91 = 78858## Starting iteration 92 / 122## neighbornet_objective for iteration 92 = 72448## Starting iteration 93 / 122## neighbornet_objective for iteration 93 = 81070## Starting iteration 94 / 122## neighbornet_objective for iteration 94 = 73675## Starting iteration 95 / 122## neighbornet_objective for iteration 95 = 73675## Starting iteration 96 / 122## neighbornet_objective for iteration 96 = 73661## Starting iteration 97 / 122## neighbornet_objective for iteration 97 = 71056## Starting iteration 98 / 122## neighbornet_objective for iteration 98 = 71003## Starting iteration 99 / 122## neighbornet_objective for iteration 99 = 89315## Starting iteration 100 / 122## neighbornet_objective for iteration 100 = 88889## Starting iteration 101 / 122## neighbornet_objective for iteration 101 = 115981## Starting iteration 102 / 122## neighbornet_objective for iteration 102 = 84167## Starting iteration 103 / 122## neighbornet_objective for iteration 103 = 83081## Starting iteration 104 / 122## neighbornet_objective for iteration 104 = 81046## Starting iteration 105 / 122## neighbornet_objective for iteration 105 = 78241## Starting iteration 106 / 122## neighbornet_objective for iteration 106 = 86713## Starting iteration 107 / 122## neighbornet_objective for iteration 107 = 73531## Starting iteration 108 / 122## neighbornet_objective for iteration 108 = 75541## Starting iteration 109 / 122## neighbornet_objective for iteration 109 = 85664## Starting iteration 110 / 122## neighbornet_objective for iteration 110 = 83844## Starting iteration 111 / 122## neighbornet_objective for iteration 111 = 84380## Starting iteration 112 / 122## neighbornet_objective for iteration 112 = 86888## Starting iteration 113 / 122## neighbornet_objective for iteration 113 = 75464## Starting iteration 114 / 122## neighbornet_objective for iteration 114 = 66759## Starting iteration 115 / 122## neighbornet_objective for iteration 115 = 75147## Starting iteration 116 / 122## neighbornet_objective for iteration 116 = 85767## Starting iteration 117 / 122## neighbornet_objective for iteration 117 = 69770## Starting iteration 118 / 122## neighbornet_objective for iteration 118 = 69836## Starting iteration 119 / 122## neighbornet_objective for iteration 119 = 69567## Starting iteration 120 / 122## neighbornet_objective for iteration 120 = 77881## Starting iteration 121 / 122## neighbornet_objective for iteration 121 = 68331## Starting iteration 122 / 122## neighbornet_objective for iteration 122 = 65695## crossing edges objective = 62905## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/GoT_NN_lannister_ending.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
Clustering
if (!requireNamespace("DuoClustering2018", quietly = TRUE)) {
stop("Package 'DuoClustering2018' is needed for this vignette. Please install it with BiocManager::install('DuoClustering2018').")
}/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
# library(DuoClustering2018)
df_long <- DuoClustering2018::clustering_summary_filteredExpr10_Koh_v2()## see ?DuoClustering2018 and browseVignettes('DuoClustering2018') for documentation## loading from cache# print(unique(df_long$method))
# graphing_columns <- c("PCAKmeans", "RtsneKmeans", "Seurat", "SC3")
graphing_columns <- c("RaceID2", "FlowSOM", "CIDR", "TSCAN", "PCAKmeans", "PCAHC", "SAFE", "pcaReduce", "RtsneKmeans", "monocle", "SC3svm", "Seurat", "SC3") # the order from the original study; ascend has no data
# shuffle graphing_columns
set.seed(43)
graphing_columns <- sample(graphing_columns)
head(df_long)## dataset method cell run k resolution cluster
## 1 sce_filteredExpr10_Koh PCAKmeans SRR3952323 1 2 NA 1
## 2 sce_filteredExpr10_Koh PCAKmeans SRR3952325 1 2 NA 1
## 3 sce_filteredExpr10_Koh PCAKmeans SRR3952326 1 2 NA 1
## 4 sce_filteredExpr10_Koh PCAKmeans SRR3952327 1 2 NA 1
## 5 sce_filteredExpr10_Koh PCAKmeans SRR3952328 1 2 NA 1
## 6 sce_filteredExpr10_Koh PCAKmeans SRR3952329 1 2 NA 1
## trueclass est_k elapsed
## 1 H7hESC NA 14.318
## 2 H7hESC NA 14.318
## 3 H7hESC NA 14.318
## 4 H7hESC NA 14.318
## 5 H7hESC NA 14.318
## 6 H7hESC NA 14.318/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
for (method_name in graphing_columns) {
cat("Method:", method_name, "\n")
df_sub <- df_long[df_long$method == method_name, ]
cat(" Unique runs: ", sort(unique(df_sub$run)), "\n")
cat(" Unique k values: ", sort(unique(df_sub$k)), "\n")
cat(" Unique resolutions:", sort(unique(df_sub$resolution)), "\n\n")
}## Method: Seurat
## Unique runs: 1 2 3 4 5
## Unique k values: 7 8 9
## Unique resolutions: 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1
##
## Method: pcaReduce
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: TSCAN
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: PCAKmeans
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: FlowSOM
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: monocle
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: RtsneKmeans
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: SC3
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: SAFE
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: CIDR
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: PCAHC
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: RaceID2
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:
##
## Method: SC3svm
## Unique runs: 1 2 3 4 5
## Unique k values: 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Unique resolutions:/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
# keep only rows where run = 1, k = 9, and (for df_long$Seurat only) resolution = 2
df_long <- df_long %>%
mutate(resolution = as.numeric(as.character(resolution))) %>%
filter(
run == 1,
k == 9,
method != "Seurat" | (method == "Seurat" & resolution == 2)
)/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
# Add a unique row index per observation
df_long <- df_long %>%
group_by(method) %>%
mutate(obs_id = row_number()) %>%
ungroup()
df <- df_long %>%
select(obs_id, trueclass, method, cluster) %>%
pivot_wider(
id_cols = c(obs_id, trueclass),
names_from = method,
values_from = cluster
) %>%
select(-obs_id) # optional: remove if obs_id is no longer needed
# df$FlowSOM <- NULL
df$ascend <- NULL
head(df)## # A tibble: 6 × 14
## trueclass PCAKmeans RtsneKmeans Seurat FlowSOM SC3 CIDR PCAHC SC3svm
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 H7hESC 4 9 5 4 4 1 1 3
## 2 H7hESC 4 9 5 4 4 1 1 3
## 3 H7hESC 4 9 5 4 4 1 1 3
## 4 H7hESC 4 9 5 4 4 1 1 3
## 5 H7hESC 4 9 5 4 4 1 1 3
## 6 H7hESC 4 9 5 4 4 1 1 3
## # ℹ 5 more variables: pcaReduce <chr>, TSCAN <chr>, SAFE <chr>, monocle <chr>,
## # RaceID2 <chr>/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
Subsample for debugging
sample_size <- NULL # NULL for full sample
if (!is.null(sample_size)) {
set.seed(42)
df <- df %>% slice_sample(n = sample_size)
} else {
sample_size <- "full"
}/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
set.seed(42)
p <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "random", coloring_algorithm = "left", resolution = 12, verbose = TRUE, output_plot_path = file.path(output_dir, sprintf("clustering_%s_random.pdf", sample_size)), min_text = 0.01, save_height = 24, save_width = 24, rasterise_alluvia = FALSE, dpi = 50, color_boxes = FALSE, color_bands = TRUE, color_band_list = c("H7hESC" = "#D55E00", "H7_derived_APS" = "#56B4E9", "H7_derived_MPS" = "#009E73", "H7_derived_DLL1pPXM" = "#F0E442", "H7_derived_ESMT" = "#0072B2", "H7_derived_Sclrtm" = "#E69F00", "H7_derived_D5CntrlDrmmtm" = "#CC79A7", "H7_derived_D2LtM" = "#666666", "H7_dreived_D2.25_Smtmrs" = "#AD7700"), color_band_column = "trueclass", text_size = 35, axis_text_size = 20, axis_text_vjust = -5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 531## m = 13## a = 137## k_1 = 9## k_2 = 9## k_3 = 9## k_4 = 9## k_5 = 9## k_6 = 9## k_7 = 9## k_8 = 9## k_9 = 7## k_10 = 9## k_11 = 9## k_12 = 9## k_13 = 9## K_sum = 115## K_prod = 1977006755367## Sorting data with sorting_algorithm=random## Determining crossing edges objective (to disable, use verbose==FALSE)## crossing edges objective = 625967## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/clustering_full_random.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
p2 <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "neighbornet", coloring_algorithm = "left", resolution = 12, verbose = TRUE, optimize_column_order = FALSE, output_plot_path = file.path(output_dir, sprintf("clustering_%s_NN.pdf", sample_size)), min_text = 0.01, save_height = 24, save_width = 24, rasterise_alluvia = FALSE, dpi = 50, color_boxes = FALSE, color_bands = TRUE, color_band_list = c("H7hESC" = "#D55E00", "H7_derived_APS" = "#56B4E9", "H7_derived_MPS" = "#009E73", "H7_derived_DLL1pPXM" = "#F0E442", "H7_derived_ESMT" = "#0072B2", "H7_derived_Sclrtm" = "#E69F00", "H7_derived_D5CntrlDrmmtm" = "#CC79A7", "H7_derived_D2LtM" = "#666666", "H7_dreived_D2.25_Smtmrs" = "#AD7700"), color_band_column = "trueclass", text_size = 35, axis_text_size = 20, axis_text_vjust = -5)## Loading in data## Preprocessing data before sorting## Alluvial statistics: n = number of elements; m = number of graphing columns; a = number of alluvia/edges; k_i = number of blocks in layer i (where i goes from 1:m); K_sum = number of blocks across all layers; K_prod = product of blocks across all layers## n = 531## m = 13## a = 137## k_1 = 9## k_2 = 9## k_3 = 9## k_4 = 9## k_5 = 9## k_6 = 9## k_7 = 9## k_8 = 9## k_9 = 7## k_10 = 9## k_11 = 9## k_12 = 9## k_13 = 9## K_sum = 115## K_prod = 1977006755367## Sorting data with sorting_algorithm=neighbornet## Running neighbornet## Running 'neighbornet' for stratum order## Cycle: CIDR~~1, monocle~~3, pcaReduce~~5, SC3~~4, Seurat~~5, TSCAN~~2, SAFE~~5, FlowSOM~~4, SC3svm~~5, SAFE~~1, RtsneKmeans~~7, PCAHC~~3, TSCAN~~1, Seurat~~3, pcaReduce~~7, CIDR~~3, PCAKmeans~~7, SC3~~5, monocle~~2, CIDR~~2, FlowSOM~~3, Seurat~~8, monocle~~8, PCAKmeans~~3, TSCAN~~4, pcaReduce~~4, SC3~~8, SC3svm~~4, monocle~~9, monocle~~4, Seurat~~6, PCAKmeans~~1, SC3svm~~6, CIDR~~9, pcaReduce~~1, FlowSOM~~2, PCAHC~~8, SC3~~9, TSCAN~~5, RtsneKmeans~~3, SAFE~~7, RaceID2~~8, PCAHC~~2, FlowSOM~~5, Seurat~~2, PCAHC~~6, RtsneKmeans~~6, TSCAN~~9, FlowSOM~~1, pcaReduce~~2, CIDR~~6, SC3~~7, SC3svm~~9, RaceID2~~9, PCAKmeans~~9, CIDR~~8, SAFE~~2, pcaReduce~~8, monocle~~6, PCAHC~~7, RaceID2~~2, FlowSOM~~7, CIDR~~7, PCAKmeans~~6, Seurat~~4, RtsneKmeans~~1, SC3~~6, SC3svm~~7, FlowSOM~~6, RaceID2~~3, TSCAN~~7, RaceID2~~5, RaceID2~~7, CIDR~~5, TSCAN~~8, FlowSOM~~9, Seurat~~7, SC3svm~~8, SC3~~2, RtsneKmeans~~5, pcaReduce~~9, PCAKmeans~~8, monocle~~1, PCAHC~~5, SAFE~~6, RaceID2~~6, PCAHC~~4, monocle~~5, PCAKmeans~~5, pcaReduce~~3, RtsneKmeans~~2, SC3~~1, SC3svm~~1, Seurat~~1, TSCAN~~3, RaceID2~~4, TSCAN~~6, RtsneKmeans~~4, Seurat~~0, SC3svm~~2, SC3~~3, pcaReduce~~6, PCAKmeans~~2, monocle~~7, PCAHC~~9, SAFE~~4, CIDR~~4, FlowSOM~~8, RtsneKmeans~~8, RaceID2~~1, SAFE~~3, SC3svm~~3, RtsneKmeans~~9, PCAKmeans~~4, PCAHC~~1## Determining optimal cycle start## Starting iteration 1 / 115## neighbornet_objective for iteration 1 = 266745## Starting iteration 2 / 115## neighbornet_objective for iteration 2 = 242075## Starting iteration 3 / 115## neighbornet_objective for iteration 3 = 234499## Starting iteration 4 / 115## neighbornet_objective for iteration 4 = 280131## Starting iteration 5 / 115## neighbornet_objective for iteration 5 = 252263## Starting iteration 6 / 115## neighbornet_objective for iteration 6 = 226083## Starting iteration 7 / 115## neighbornet_objective for iteration 7 = 217050## Starting iteration 8 / 115## neighbornet_objective for iteration 8 = 195925## Starting iteration 9 / 115## neighbornet_objective for iteration 9 = 211371## Starting iteration 10 / 115## neighbornet_objective for iteration 10 = 202535## Starting iteration 11 / 115## neighbornet_objective for iteration 11 = 279791## Starting iteration 12 / 115## neighbornet_objective for iteration 12 = 356664## Starting iteration 13 / 115## neighbornet_objective for iteration 13 = 375476## Starting iteration 14 / 115## neighbornet_objective for iteration 14 = 397544## Starting iteration 15 / 115## neighbornet_objective for iteration 15 = 427264## Starting iteration 16 / 115## neighbornet_objective for iteration 16 = 376293## Starting iteration 17 / 115## neighbornet_objective for iteration 17 = 313519## Starting iteration 18 / 115## neighbornet_objective for iteration 18 = 263827## Starting iteration 19 / 115## neighbornet_objective for iteration 19 = 186571## Starting iteration 20 / 115## neighbornet_objective for iteration 20 = 127180## Starting iteration 21 / 115## neighbornet_objective for iteration 21 = 114042## Starting iteration 22 / 115## neighbornet_objective for iteration 22 = 135799## Starting iteration 23 / 115## neighbornet_objective for iteration 23 = 146759## Starting iteration 24 / 115## neighbornet_objective for iteration 24 = 125747## Starting iteration 25 / 115## neighbornet_objective for iteration 25 = 122360## Starting iteration 26 / 115## neighbornet_objective for iteration 26 = 119258## Starting iteration 27 / 115## neighbornet_objective for iteration 27 = 109616## Starting iteration 28 / 115## neighbornet_objective for iteration 28 = 109694## Starting iteration 29 / 115## neighbornet_objective for iteration 29 = 110120## Starting iteration 30 / 115## neighbornet_objective for iteration 30 = 108977## Starting iteration 31 / 115## neighbornet_objective for iteration 31 = 203818## Starting iteration 32 / 115## neighbornet_objective for iteration 32 = 227260## Starting iteration 33 / 115## neighbornet_objective for iteration 33 = 274913## Starting iteration 34 / 115## neighbornet_objective for iteration 34 = 299303## Starting iteration 35 / 115## neighbornet_objective for iteration 35 = 350799## Starting iteration 36 / 115## neighbornet_objective for iteration 36 = 349711## Starting iteration 37 / 115## neighbornet_objective for iteration 37 = 306590## Starting iteration 38 / 115## neighbornet_objective for iteration 38 = 302044## Starting iteration 39 / 115## neighbornet_objective for iteration 39 = 349240## Starting iteration 40 / 115## neighbornet_objective for iteration 40 = 300742## Starting iteration 41 / 115## neighbornet_objective for iteration 41 = 248734## Starting iteration 42 / 115## neighbornet_objective for iteration 42 = 205462## Starting iteration 43 / 115## neighbornet_objective for iteration 43 = 187319## Starting iteration 44 / 115## neighbornet_objective for iteration 44 = 190849## Starting iteration 45 / 115## neighbornet_objective for iteration 45 = 193892## Starting iteration 46 / 115## neighbornet_objective for iteration 46 = 210108## Starting iteration 47 / 115## neighbornet_objective for iteration 47 = 244884## Starting iteration 48 / 115## neighbornet_objective for iteration 48 = 249967## Starting iteration 49 / 115## neighbornet_objective for iteration 49 = 295672## Starting iteration 50 / 115## neighbornet_objective for iteration 50 = 296150## Starting iteration 51 / 115## neighbornet_objective for iteration 51 = 255553## Starting iteration 52 / 115## neighbornet_objective for iteration 52 = 254888## Starting iteration 53 / 115## neighbornet_objective for iteration 53 = 245794## Starting iteration 54 / 115## neighbornet_objective for iteration 54 = 252696## Starting iteration 55 / 115## neighbornet_objective for iteration 55 = 213700## Starting iteration 56 / 115## neighbornet_objective for iteration 56 = 171262## Starting iteration 57 / 115## neighbornet_objective for iteration 57 = 180906## Starting iteration 58 / 115## neighbornet_objective for iteration 58 = 176510## Starting iteration 59 / 115## neighbornet_objective for iteration 59 = 230510## Starting iteration 60 / 115## neighbornet_objective for iteration 60 = 287955## Starting iteration 61 / 115## neighbornet_objective for iteration 61 = 319415## Starting iteration 62 / 115## neighbornet_objective for iteration 62 = 325665## Starting iteration 63 / 115## neighbornet_objective for iteration 63 = 326482## Starting iteration 64 / 115## neighbornet_objective for iteration 64 = 284047## Starting iteration 65 / 115## neighbornet_objective for iteration 65 = 324551## Starting iteration 66 / 115## neighbornet_objective for iteration 66 = 295439## Starting iteration 67 / 115## neighbornet_objective for iteration 67 = 295545## Starting iteration 68 / 115## neighbornet_objective for iteration 68 = 235407## Starting iteration 69 / 115## neighbornet_objective for iteration 69 = 242831## Starting iteration 70 / 115## neighbornet_objective for iteration 70 = 195061## Starting iteration 71 / 115## neighbornet_objective for iteration 71 = 193749## Starting iteration 72 / 115## neighbornet_objective for iteration 72 = 128577## Starting iteration 73 / 115## neighbornet_objective for iteration 73 = 136551## Starting iteration 74 / 115## neighbornet_objective for iteration 74 = 118438## Starting iteration 75 / 115## neighbornet_objective for iteration 75 = 165621## Starting iteration 76 / 115## neighbornet_objective for iteration 76 = 205921## Starting iteration 77 / 115## neighbornet_objective for iteration 77 = 246841## Starting iteration 78 / 115## neighbornet_objective for iteration 78 = 267825## Starting iteration 79 / 115## neighbornet_objective for iteration 79 = 265255## Starting iteration 80 / 115## neighbornet_objective for iteration 80 = 306793## Starting iteration 81 / 115## neighbornet_objective for iteration 81 = 306793## Starting iteration 82 / 115## neighbornet_objective for iteration 82 = 267413## Starting iteration 83 / 115## neighbornet_objective for iteration 83 = 227483## Starting iteration 84 / 115## neighbornet_objective for iteration 84 = 185927## Starting iteration 85 / 115## neighbornet_objective for iteration 85 = 158649## Starting iteration 86 / 115## neighbornet_objective for iteration 86 = 114156## Starting iteration 87 / 115## neighbornet_objective for iteration 87 = 145897## Starting iteration 88 / 115## neighbornet_objective for iteration 88 = 164859## Starting iteration 89 / 115## neighbornet_objective for iteration 89 = 232495## Starting iteration 90 / 115## neighbornet_objective for iteration 90 = 284965## Starting iteration 91 / 115## neighbornet_objective for iteration 91 = 347085## Starting iteration 92 / 115## neighbornet_objective for iteration 92 = 347085## Starting iteration 93 / 115## neighbornet_objective for iteration 93 = 323523## Starting iteration 94 / 115## neighbornet_objective for iteration 94 = 307473## Starting iteration 95 / 115## neighbornet_objective for iteration 95 = 273655## Starting iteration 96 / 115## neighbornet_objective for iteration 96 = 222693## Starting iteration 97 / 115## neighbornet_objective for iteration 97 = 260801## Starting iteration 98 / 115## neighbornet_objective for iteration 98 = 317407## Starting iteration 99 / 115## neighbornet_objective for iteration 99 = 358106## Starting iteration 100 / 115## neighbornet_objective for iteration 100 = 396016## Starting iteration 101 / 115## neighbornet_objective for iteration 101 = 379988## Starting iteration 102 / 115## neighbornet_objective for iteration 102 = 414076## Starting iteration 103 / 115## neighbornet_objective for iteration 103 = 342072## Starting iteration 104 / 115## neighbornet_objective for iteration 104 = 342966## Starting iteration 105 / 115## neighbornet_objective for iteration 105 = 365174## Starting iteration 106 / 115## neighbornet_objective for iteration 106 = 363918## Starting iteration 107 / 115## neighbornet_objective for iteration 107 = 349962## Starting iteration 108 / 115## neighbornet_objective for iteration 108 = 253767## Starting iteration 109 / 115## neighbornet_objective for iteration 109 = 135471## Starting iteration 110 / 115## neighbornet_objective for iteration 110 = 101256## Starting iteration 111 / 115## neighbornet_objective for iteration 111 = 119325## Starting iteration 112 / 115## neighbornet_objective for iteration 112 = 146191## Starting iteration 113 / 115## neighbornet_objective for iteration 113 = 144595## Starting iteration 114 / 115## neighbornet_objective for iteration 114 = 196587## Starting iteration 115 / 115## neighbornet_objective for iteration 115 = 245923## crossing edges objective = 101256## Complete with sorting## Plotting data## Saving plot to=/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T//Rtmp1kOc9s/vignette_output/clustering_full_NN.pdf## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.p2## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
Optional (only if I’m doing a subset of objectives)
# log_msgs <- character()
#
# withCallingHandlers({
# p2 <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "neighbornet", verbose=TRUE, optimize_column_order = FALSE, output_plot_path=file.path(output_dir, sprintf("clustering_%s_NN.pdf", sample_size)), min_text = 0.01, save_height = 24, save_width = 24, rasterise_alluvia = FALSE, dpi = 50, color_boxes = FALSE, color_bands = TRUE, color_band_list = c("H7hESC" = "#D55E00", "H7_derived_APS" = "#56B4E9", "H7_derived_MPS" = "#009E73", "H7_derived_DLL1pPXM" = "#F0E442", "H7_derived_ESMT" = "#0072B2", "H7_derived_Sclrtm" = "#E69F00", "H7_derived_D5CntrlDrmmtm" = "#CC79A7", "H7_derived_D2LtM" = "#666666", "H7_dreived_D2.25_Smtmrs" = "#AD7700"), color_band_column = 'trueclass')
# }, message = function(m) {
# log_msgs <<- c(log_msgs, conditionMessage(m))
# # no muffling → still prints to console
# })
# p2/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
# objectives <- stringr::str_match(log_msgs, "neighbornet_objective for iteration \\d+ = (\\d+)")[, 2]
# objectives <- as.numeric(na.omit(objectives))
#
# df_obj <- data.frame(
# iteration = seq_along(objectives),
# objective = objectives
# )
#
# ggplot(df_obj, aes(x = iteration, y = objective)) +
# geom_line(color = "blue") +
# scale_x_continuous(
# breaks = seq(0, length(objectives), by = 5), # major ticks
# minor_breaks = seq(0, length(objectives), by = 1) # minor ticks
# ) +
# labs(title = "NeighborNet Objective Over Iterations",
# x = "Iteration", y = "Objective") +
# theme_minimal()/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
cycle_start_positions <- NULL # c(110) - 110 for NN, 34 for TSP # c(12:25, 130:150) # c(40:60, 140:160) # choose based on finding some local optima above
column_sorting_metric <- "edge_crossing" # edge_crossing or ARI
optimize_column_order_per_cycle <- TRUE
set.seed(42)
p3 <- plot_alluvial(df, graphing_columns = graphing_columns, sorting_algorithm = "neighbornet", coloring_algorithm = "left", resolution = 12, verbose = TRUE, optimize_column_order = TRUE, optimize_column_order_per_cycle = optimize_column_order_per_cycle, output_plot_path = file.path(output_dir, sprintf("clustering_%s_NN_optimized_columns.pdf", sample_size)), min_text = 0.01, save_height = 24, save_width = 24, rasterise_alluvia = FALSE, dpi = 50, color_boxes = FALSE, color_bands = TRUE, color_band_list = c("H7hESC" = "#D55E00", "H7_derived_APS" = "#56B4E9", "H7_derived_MPS" = "#009E73", "H7_derived_DLL1pPXM" = "#F0E442", "H7_derived_ESMT" = "#0072B2", "H7_derived_Sclrtm" = "#E69F00", "H7_derived_D5CntrlDrmmtm" = "#CC79A7", "H7_derived_D2LtM" = "#666666", "H7_dreived_D2.25_Smtmrs" = "#AD7700"), color_band_column = "trueclass", cycle_start_positions = cycle_start_positions, column_sorting_metric = column_sorting_metric, text_size = 35, axis_text_size = 20, axis_text_vjust = -5)
p3/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
Make legend
# Ensure color_band_list is a named vector
color_band_list <- c(
"Lannister" = "#D55E00",
"Westeros" = "#56B4E9"
)
# Make a dummy data frame just for the legend
legend_df <- data.frame(Gene = names(color_band_list))
# Plot with invisible points to show only the legend
legend_plot <- ggplot(legend_df, aes(x = 1, y = Gene, color = Gene)) +
geom_line(linewidth = 5) +
scale_color_manual(values = color_band_list, name = "Affiliation") +
theme_void() +
theme(legend.position = "right")
ggsave(file.path(output_dir, "GoT_legend.pdf"), plot = legend_plot)## Saving 8 x 5 in image
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
# Ensure color_band_list is a named vector
color_band_list <- c(
"H7hESC" = "#D55E00",
"H7_derived_APS" = "#56B4E9",
"H7_derived_MPS" = "#009E73",
"H7_derived_DLL1pPXM" = "#F0E442",
"H7_derived_ESMT" = "#0072B2",
"H7_derived_Sclrtm" = "#E69F00",
"H7_derived_D5CntrlDrmmtm" = "#CC79A7",
"H7_derived_D2LtM" = "#666666",
"H7_dreived_D2.25_Smtmrs" = "#AD7700"
)
# Make a dummy data frame just for the legend
legend_df <- data.frame(trueclass = names(color_band_list))
# Plot with invisible points to show only the legend
legend_plot <- ggplot(legend_df, aes(x = 1, y = trueclass, color = trueclass)) +
geom_line(linewidth = 5) +
scale_color_manual(values = color_band_list, name = "True Class") +
theme_void() +
theme(legend.position = "right")
ggsave(file.path(output_dir, sprintf("clustering_%s_legend.pdf", sample_size)), plot = legend_plot)## Saving 8 x 5 in image
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R
sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.3.1 (2023-06-16)
## os macOS Ventura 13.5.2
## system aarch64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Los_Angeles
## date 2025-08-21
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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## digest 0.6.33 2023-07-07 [2] CRAN (R 4.3.0)
## dplyr * 1.1.3 2023-09-03 [2] CRAN (R 4.3.0)
## DuoClustering2018 * 1.20.0 2023-10-26 [2] Bioconductor
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## ragg 1.2.5 2023-01-12 [2] CRAN (R 4.3.0)
## rappdirs 0.3.3 2021-01-31 [2] CRAN (R 4.3.0)
## Rcpp 1.0.11 2023-07-06 [2] CRAN (R 4.3.0)
## RCurl 1.98-1.12 2023-03-27 [2] CRAN (R 4.3.0)
## remotes 2.4.2.1 2023-07-18 [2] CRAN (R 4.3.0)
## reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.3.0)
## reticulate * 1.32.0 2023-09-11 [2] CRAN (R 4.3.1)
## rlang 1.1.1 2023-04-28 [2] CRAN (R 4.3.0)
## rmarkdown 2.24 2023-08-14 [2] CRAN (R 4.3.0)
## rprojroot 2.0.3 2022-04-02 [2] CRAN (R 4.3.0)
## RSQLite 2.3.1 2023-04-03 [2] CRAN (R 4.3.0)
## rstudioapi 0.15.0 2023-07-07 [2] CRAN (R 4.3.0)
## S4Vectors 0.39.1 2023-06-02 [2] Bioconductor
## sass 0.4.7 2023-07-15 [2] CRAN (R 4.3.0)
## scales 1.2.1 2022-08-20 [2] CRAN (R 4.3.0)
## sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.3.0)
## shiny 1.7.5 2023-08-12 [2] CRAN (R 4.3.0)
## stringi 1.7.12 2023-01-11 [2] CRAN (R 4.3.0)
## stringr 1.5.0 2022-12-02 [2] CRAN (R 4.3.0)
## systemfonts 1.0.4 2022-02-11 [2] CRAN (R 4.3.0)
## testthat * 3.1.10 2023-07-06 [2] CRAN (R 4.3.0)
## textshaping 0.3.6 2021-10-13 [2] CRAN (R 4.3.0)
## tibble * 3.2.1 2023-03-20 [2] CRAN (R 4.3.0)
## tidyr * 1.3.0 2023-01-24 [2] CRAN (R 4.3.0)
## tidyselect 1.2.0 2022-10-10 [2] CRAN (R 4.3.0)
## TSP 1.2-5 2025-05-27 [2] CRAN (R 4.3.3)
## tweenr 2.0.3 2024-02-26 [2] CRAN (R 4.3.1)
## urlchecker 1.0.1 2021-11-30 [2] CRAN (R 4.3.0)
## usethis 3.1.0 2024-11-26 [2] CRAN (R 4.3.3)
## utf8 1.2.3 2023-01-31 [2] CRAN (R 4.3.0)
## vctrs 0.6.3 2023-06-14 [2] CRAN (R 4.3.0)
## viridis 0.6.4 2023-07-22 [2] CRAN (R 4.3.0)
## viridisLite 0.4.2 2023-05-02 [2] CRAN (R 4.3.0)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.3.0)
## P wompwomp * 0.99.0 2025-08-22 [?] Bioconductor
## xfun 0.52 2025-04-02 [2] CRAN (R 4.3.3)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.3.0)
## XVector 0.41.1 2023-06-02 [2] Bioconductor
## yaml 2.3.7 2023-01-23 [2] CRAN (R 4.3.0)
## zlibbioc 1.47.0 2023-05-20 [2] Bioconductor
##
## [1] /private/var/folders/wr/wdr46lxn7fbc_kty6kwbfhxc0000gn/T/Rtmpg9hbdf/temp_libpathc9607864a1d
## [2] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
##
## P ── Loaded and on-disk path mismatch.
##
## ─ Python configuration ───────────────────────────────────────────────────────
## python: /Users/joeyrich/miniconda3/envs/wompwomp_env/bin/python
## libpython: /Users/joeyrich/miniconda3/envs/wompwomp_env/lib/libpython3.10.dylib
## pythonhome: /Users/joeyrich/miniconda3/envs/wompwomp_env:/Users/joeyrich/miniconda3/envs/wompwomp_env
## version: 3.10.16 (main, Dec 11 2024, 10:22:29) [Clang 14.0.6 ]
## numpy: /Users/joeyrich/miniconda3/envs/wompwomp_env/lib/python3.10/site-packages/numpy
## numpy_version: 1.23.5
## splitspy: /Users/joeyrich/miniconda3/envs/wompwomp_env/lib/python3.10/site-packages/splitspy
##
## NOTE: Python version was forced by use_python() function
##
## ──────────────────────────────────────────────────────────────────────────────/Users/joeyrich/Desktop/local/wompwomp/vignettes/examples-with-large-data.R